At the time an oil or gas field is being appraised or developed, the development of a reservoir model usually centers on the task of building computer models suitable for forward flow simulation. Prior to this, much of the work will have focused on data acquisition and interpretation, and the construction of models suitable for simple volumetric calculations or drilling decisions. In particular, much of the interpretative work will be based on surface seismic data, and this is routinely fed into various inversion routines which produce pointwise (trace-local) estimates of the properties of direct interest, such as surface positions, layer thicknesses, hydrocarbon content, net-to-gross (NG or NG), etc.
In previous publications (Gunning and Glinsky, 2004, 2005; Gunning, 2003), the inventors have introduced an open source tool Delivery that enables users to perform a fully probabilistic seismic inversion for a layer-based model of the reservoir. This is a trace-based inversion, so it produces an ensemble of realisations of the relevant reservoir parameters at each point in the imaged seismic grid over a field. The ‘meso-scale’ layer resolution is usually around 5-20 m. At each ‘common mid-point’ (CMP) location, the inversion provides a full joint-probability distribution of quantities like the layer thicknesses, fluid content, NG, layer times, and velocities. The seismic inversion data produced by Delivery is an array of trace-local stochastic samples from a Bayesian posterior distribution of reservoir layer parameters, which contains complex correlations between layers boundaries, rock properties and fluid information, but no transverse correlations. This inversion data produced by the program is suitable for answering the simple kind of questions mentioned above, such as pointwise histograms of layer thickness, maps of hydrocarbon probability etc, but is not directly suitable for flow calculations per se.
In the past, trend maps have been formed from the results of seismic inversion for properties such as porosity and net sand. These trend maps have then been used to control the geostatistical population of properties and/or objects into the reservoir simulation model. However, none of the rich inter-property and inter-layer correlations were respected. The models have also tended to be built at extremely fine scale (less then a few meters) then upscaled to a coarser scale for reservoir simulation (less than tens of meters).
It would therefore be desirable to provide a method for converting seismic inversion data into a form suitable for use in reservoir models for flow simulation and/or volumetric calculations, while preserving the correlations available from the seismic inversion, and also honouring measured well data and preferably other geological constraints. In particular, it would be desirable to provide such a method which makes it possible to estimate and/or reduce uncertainty in the reservoir model.